Hypernetwork Dismantling via Deep Reinforcement Learning

Hypernetwork Dismantling via Deep Reinforcement Learning

Network dismantling aims to degrade the connectivity of a network by removing an optimal set of nodes. It has been widely adopted in many real-world applications such as epidemic control and rumor containment. However, conventional methods usually focus on simple network modeling with only pairwise...

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Bibliographic Details
Main Authors: He, Q. (Author), Xie, W. (Author), Yan, D. (Author), Yang, Y. (Author), Zhang, Y. (Author)
Format: Article
Language:English
Published: IEEE Computer Society 2022
Subjects:
Combinatorial optimization
Conventional methods
Costs
Deep learning
Deep Reinforcement Learning
Disease control
Epidemic control
Epidemics
Graph combinatorial optimization
Graph Combinatorial Optimization
Hypernetwork
Hypernetwork dismantling
Hypernetwork Dismantling
Job analysis
Noise measurement
Noise measurements
Optimal sets
Optimisations
Optimization
Real-world
Reinforcement learning
Task analysis
Topology
Vector spaces
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